English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cricket betting is a multi-billion dollar market. Therefore, there is astrong incentive for models that can predict the outcomes of games and beat theodds provided by bookers. The aim of this study was to investigate to whatdegree it is possible to predict the outcome of cricket matches. The targetcompetition was the English twenty over county cricket cup. The originalfeatures alongside engineered features gave rise to more than 500 team andplayer statistics. The models were optimized firstly with team features onlyand then both team and player features. The performance of the models wastested over individual seasons from 2009 to 2014 having been trained overprevious season data in each case. The optimal model was a simple predictionmethod combined with complex hierarchical features and was shown tosignificantly outperform a gambling industry benchmark.

Using Machine Learning to Predict the Outcome of English County twenty over Cricket Matches